Computed Tomography Image Recognition with Convolutional Neural Network Using Wearable Sensors

We propose a modified convolutional neural network (CNN) tailor-made for computed tomography (CT) image disease recognition to assist doctors in disease diagnosis. First, we analyze the effects of varying the CNN activation function and pooling parameters, as well as the CNN's performance using...

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Bibliographic Details
Published in:Sensors and materials 2020-10, Vol.32 (10), p.3517
Main Authors: He, Yuqing, Lei, Lei, Yang, Guangsong, Chen, Chih-Cheng, Chan, Christopher Chun Ki, Lai, Kuei-Kuei
Format: Article
Language:English
Subjects:
Artificial neural networks
Computed tomography
Feature extraction
Image enhancement
Medical imaging
Neural networks
Object recognition
Online Access:Get full text
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Summary:We propose a modified convolutional neural network (CNN) tailor-made for computed tomography (CT) image disease recognition to assist doctors in disease diagnosis. First, we analyze the effects of varying the CNN activation function and pooling parameters, as well as the CNN's performance using one data set. Second, we address the activation error that occurs when the sample data size is increased by preprocessing images by an enhancement technique, adjusting the activation function and initialization weighting, training/testing the target, and adaptively extracting features. We found that our method alleviates overfitting with these techniques. The experimental results show that our proposed scheme improves the recognition rate and can better generalize findings.
ISSN:0914-4935
DOI:10.18494/SAM.2020.2933